A framework for analysis of dynamic social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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Social network dynamic behavior is an important aspect to consider for modeling and identifying more realistically the interesting phenomena as an underlying core structure, giving more significant estimations and results. Parameters are proposed to characterize different groupings behaviors and locations in time on the network modeled by a weighted temporal graph model. A container is detected as a critical path in the model within which, a persistent structure should be deeply encapsulated and balancing between the parameters. This is a largest stable and central composition throughout an observation period expressing significantly an identity which may be acquired to be a core structure. Such identity can be enhanced toward characterizing a more significant core identity around that, a developing process of dynamic social network occurs in time.